We introduce a novel graph-based framework for abstractive meeting speech
summarization that is fully unsupervised and does not rely on any annotations.
Our work combines the strengths of multiple recent approaches while addressing
their weaknesses. Moreover, we leverage recent advances in word embeddings and
graph degeneracy applied to NLP to take exterior semantic knowledge into
account, and to design custom diversity and informativeness measures.
Experiments on the AMI and ICSI corpus show that our system improves on the
state-of-the-art. Code and data are publicly available, and our system can be
interactively tested.

我们提出了一种新颖的基于图形的框架，用于摘要会议演讲，完全不需要监督和任何注释。我们的工作结合了多种最近的方法的优点，同时解决了它们的弱点。此外，我们利用最近在 NLP 中应用的字嵌入和图形退化的进展，考虑外部语义知识，并设计了自定义的多样性和信息量度量。在 AMI 和 ICSI 语料库上的实验表明，我们的系统改进了现有技术水平。代码和数据可公开获取，我们的系统可以交互式测试。